The Modern Data Platform and LookML
Earned Mar 17, 2025 EDT
Natural Language Processing on Google Cloud
Earned Mar 10, 2025 EDT
Feature Engineering
Earned Mar 8, 2025 EST
建立圖像說明生成模型
Earned Mar 8, 2025 EST
Production Machine Learning Systems
Earned Mar 8, 2025 EST
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Mar 7, 2025 EST
Launching into Machine Learning
Earned Mar 7, 2025 EST
Building AI with Colab Enterprise
Earned Mar 7, 2025 EST
Vector Search and Embeddings in Product Search
Earned Mar 2, 2025 EST
運用 BigQuery 建立嵌入項目、向量搜尋和 RAG
Earned Mar 2, 2025 EST
Document AI: Building a Custom Document Extractor
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Process Data with Dataproc
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Optimize in BigQuery
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Design in BigQuery
Earned Feb 27, 2025 EST
Data Warehousing for Partners: BigQuery Extended Capabilities
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Enable Google Cloud Customers
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Streaming Analytics
Earned Feb 27, 2025 EST
Evaluate Your Cloud Next Generation Firewall Needs
Earned Feb 23, 2025 EST
Introduction to Cloud Next Generation Firewall
Earned Feb 23, 2025 EST
Unlocking the Power of Google Cloud Generative AI for Partners
Earned Feb 22, 2025 EST
Google Cloud Generative AI Trailblazer
Earned Feb 22, 2025 EST
Security Best Practices in Google Cloud
Earned Feb 22, 2025 EST
生成式 AI 適用的機器學習運作 (MLOps)
Earned Feb 20, 2025 EST
Vertex AI Search for Commerce
Earned Feb 19, 2025 EST
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Feb 19, 2025 EST
Deploy Google Agentspace
Earned Feb 18, 2025 EST
運用 Gemini Enterprise 加速知識交流
Earned Feb 18, 2025 EST
Recommendations with AI Applications
Earned Feb 18, 2025 EST
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned Feb 18, 2025 EST
Generative Playbooks
Earned Feb 16, 2025 EST
Accelerate Your Creativity with Text-to-Image using Imagen
Earned Feb 16, 2025 EST
Customer Experience with Google AI Architecture
Earned Feb 13, 2025 EST
Conversational AI Voice and Chat Integrations
Earned Feb 12, 2025 EST
Building Complex Self-Service Experiences in Conversational Agents
Earned Feb 11, 2025 EST
Search with AI Applications
Earned Feb 1, 2025 EST
在 BigQuery 使用 Gemini 模型
Earned Jan 23, 2025 EST
Networking in Google Cloud: Network Architecture
Earned Oct 4, 2024 EDT
Networking in Google Cloud: Routing and Addressing
Earned Oct 4, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Sep 27, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Jun 16, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Jun 15, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 15, 2024 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jun 1, 2024 EDT
Gemini in Google Meet
Earned May 30, 2024 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned May 16, 2024 EDT
Migrating VMware to Google Cloud VMware Engine
Earned May 16, 2024 EDT
Modernizing Mainframe Applications with Google Cloud
Earned May 16, 2024 EDT
Developing a Google SRE Culture
Earned Apr 12, 2024 EDT
Achieving Business Outcomes with Looker
Earned Apr 3, 2024 EDT
Digital Transformation with Google Cloud
Earned Mar 18, 2024 EDT
Trust and Security with Google Cloud
Earned Mar 18, 2024 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Mar 18, 2024 EDT
Innovating with Google Cloud Artificial Intelligence
Earned Mar 18, 2024 EDT
Scaling with Google Cloud Operations
Earned Mar 17, 2024 EDT
Exploring Data Transformation with Google Cloud
Earned Mar 17, 2024 EDT
Analyzing and Visualizing Data in Looker
Earned Mar 17, 2024 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Mar 16, 2024 EDT
BI Reporting: Looker Visualization on BigQuery
Earned Mar 16, 2024 EDT
Certification Learning Path: Professional Cloud DevOps Engineer
Earned Mar 13, 2024 EDT
Managing Security in Google Cloud
Earned Mar 1, 2024 EST
Gemini in Google Sheets
Earned Jan 12, 2024 EST
Gemini in Google Docs
Earned Jan 12, 2024 EST
Gemini in Gmail
Earned Jan 12, 2024 EST
Gemini for Google Workspace 簡介
Earned Jan 12, 2024 EST
編碼器-解碼器架構
Earned Dec 28, 2023 EST
注意力機制
Earned Dec 28, 2023 EST
圖像生成簡介
Earned Dec 28, 2023 EST
負責任的 AI 技術簡介
Earned Dec 22, 2023 EST
Certification Learning Path: Professional Cloud Security Engineer
Earned Oct 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 29, 2023 EDT
Security Practices with Google Security Operations - SIEM
Earned Sep 25, 2023 EDT
SAP on Google Cloud
Earned Sep 21, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Sep 6, 2023 EDT
This course will familiarize you with the core functionality of Chronicle, including the user interface, connections, and settings.
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本課程將複習 Model Armor 的基本安全功能，讓您具備使用這項服務的能力。您將瞭解 LLM 的相關安全風險，以及 Model Armor 如何保護 AI 應用程式。
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Google Threat Intelligence 在掌握威脅方面首屈一指，為全球的資安團隊提供詳細、及時的威脅情報。這個課程介紹 Google Threat Intelligence 的多項功能，以及組織可以透過哪些常見方法，運用這項產品主動減輕威脅。
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In the context of a real-world use case, learn how to use Security Command Center’s virtual red teaming feature to identify risks. Then, learn how attack exposure scores help you prioritize issues and how risk reports keep stakeholders in the loop.
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Learn how to use NotebookLM to create a personalized study guide for the Professional Security Operations Engineer certification exam.
You'll review NotebookLM features, create a notebook in NotebookLM, and learn how to use a study guide to practice for a certification exam.
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Model Garden is a model library that helps you discover, test, and deploy models from Google and Google partners. Learn how to explore the available models and select the right ones for your use case. And how to deploy and interact with Model Garden models through the Google Cloud console and APIs.
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Learn a variety of strategies and techniques to engineer effective prompts for generative models
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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.
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This course delves into the complexities of assessing the quality of large language model outputs. It examines the challenges enterprises face due to the subjective and sometimes incorrect nature of LLM responses, including hallucinations and inconsistent results. The course introduces various evaluation metrics for different tasks like classification, text generation, and question answering, such as Accuracy, Precision, Recall, F1 score, ROUGE, BLEU, and Exact Match. It also explores evaluation methods offered by Vertex AI LLM Evaluation Services, including computation-based, autorater, and human evaluation, providing insights into their application and benefits. Finally, the module covers how to unit test LLM applications within Vertex AI.
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Data stores represent a simple way to make content available to many types of generative AI applications, including search applications, recommendations engines, Gemini Enterprise apps, Agent Development Kit agents, and apps built with Google Gen AI or LangChain SDKs. Connect data from many sources include Cloud Storage, Google Drive, chat apps, mail apps, ticketing systems, third-party file storage providers, Salesforce, and many more.
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Learn how to create Hybrid Search applications using Vertex AI Vertex Search to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.
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Explore the four pillars of Enterprise Readiness in generative AI: data governance and privacy, security and compliance support, infrastructure reliability and sustainability, and responsible AI. You will also learn how these pillars address concerns about data privacy and security. Learn about customizing foundation models with your data while keeping your data safe using adapter layers, how to keep your AI models safe and compliant when deploying them across the world, and the multiple layers of encryption, rigorous controls, supply chain audits, and ongoing security testing that are built into Google Cloud. You will also learn about security controls such as VPC, customer-managed encryption keys, access transparency, and data residency zones. And explore enterprise controls, certifications, and responsible AI tooling available in Vertex AI to ensure your data remains secure and compliant with global regulations when deploying generative AI models.
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Evaluation is important at every step of your Gen AI development process. In this course you will learn how to evaluate gen AI agents built using agent frameworks.
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In this course, you will learn how to easily scale AI from laptop to Cloud by bringing Ray and Vertex AI together. You will learn how to create a Ray cluster, connect to it, and run some simple Ray code. You will also learn how to integrate BigQuery seamlessly with Ray data.
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Learn how to build your own Retrieval-Augmented Generation (RAG) solutions for greater control and flexibility than out-of-the-box implementations. Create a custom RAG solution using Vertex AI APIs, vector stores, and the LangChain framework.
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Initial deployment of Vertex AI Search and Gemini Enterprise apps takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience.
In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini Enterprise apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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In this Google DeepMind course you will discover the mechanisms of the transformer architecture. You will investigate how transformer language models process prompts to make context-sensitive next-token predictions. Through practical activities you will explore the attention mechanism, visualize attention weights, and encounter advanced concepts like masked attention and multi-head attention. You will also learn other techniques that are necessary to build neural networks that are well-suited to be used as language models. Finally, through activities on values, stakeholder mapping and community engagement, you will practice concrete tools for ensuring AI projects are developed with communities, not just for them.
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AI Applications provides built-in analytics for your Vertex AI Search and Gemini Enterprise apps. Learn what metrics are tracked and how to view them in this course.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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This course dives into the world of media creation in Vertex AI using Nano Banana and Veo. Learn to design text and image-based prompts to produce high-quality, consistent images, and captivating, cinematic video clips. You'll also learn to refine generated assets using core editing functions. Finally, this course guides you through multi-tool workflow implementations for creative control and consistency, empowering you to transform images into video clips and leverage Gemini for prompt writing assistance and feedback.
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本課程會說明 Gemini in BigQuery，這是一套由 AI 輔助的功能，可協助「從資料到 AI」的工作流程。這些功能包含資料探索和準備、程式碼生成和疑難排解，以及工作流程探索和視覺化。本課程將透過概念解說、應用實例和實作實驗室，協助資料從業人員提升工作效率，並加速開發 pipeline。
This course explores how to implement a streaming analytics solution using Pub/Sub.
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NotebookLM is an AI-powered collaborator that helps you do your best thinking. After uploading your documents, NotebookLM becomes an instant expert in those sources so you can read, take notes, and collaborate with it to refine and organize your ideas.
NotebookLM Pro gives you everything already included with NotebookLM, as well as higher utilization limits, access to premium features, and additional sharing options and analytics.
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Migration from Azure to Google Cloud Compute Engine using Migrate to Virtual Machines (v5) using demo VM(s). It provides a proof-of-concept that walks you through the process of replicating a VM to doing test cutover and final cutover of the VM.
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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。
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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。
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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search.
You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.
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這堂課程會介紹 AI 搜尋技術、工具和應用程式。主題涵蓋使用向量嵌入執行語意搜尋；結合語意和關鍵字做法的混合型搜尋機制；以及運用檢索增強生成 (RAG) 技術建構有基準的 AI 代理，盡可能減少 AI 幻覺。您可以實際使用 Vertex AI Vector Search，打造智慧型搜尋引擎。
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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。
完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Dataproc 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.
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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
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本課程針對評估生成式和預測式 AI 模型，向機器學習從業人員介紹相關的基礎工具、技術和最佳做法。模型評估是機器學習的重要領域，確保這類系統能在正式環境中提供可靠、準確且成效優異的結果。
學員將深入瞭解多種評估指標與方法，以及適用於不同模型類型和工作的應用方式。此外，也會特別介紹生成式 AI 模型帶來的獨特難題，並提供有效的應對策略。透過 Google Cloud Vertex AI 平台，學員將瞭解在模型挑選、最佳化和持續監控方面，該如何導入穩健的評估程序。
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本課程旨在說明 AI 的可解釋性和透明度概念、探討 AI 透明度對開發人員和工程師的重要性。課程中也會介紹實務方法和工具，有助於讓資料和 AI 模型透明且可解釋。
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本課程涵蓋「AI 隱私權」和「AI 安全性」這兩個重要主題。我們將介紹實用的方法和工具，協助您運用 Google Cloud 產品和開放原始碼工具，導入 AI 隱私權和安全性的建議做法。
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本課程旨在說明負責任 AI 技術的概念和 AI 開發原則，同時介紹各項技術，在實務上找出公平性和偏誤，減少 AI/機器學習做法上的偏誤。我們也將探討實用方法和工具，透過 Google Cloud 產品和開放原始碼工具，導入負責任 AI 技術的最佳做法。
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大型語言模型 (LLM) 誕生之後，生成式 AI 應用程式帶來的嶄新使用者體驗，可說是幾乎前所未有。身為應用程式開發人員，您要如何在 Google Cloud，運用生成式 AI 建立出色的互動式應用程式？
本課程將帶您瞭解生成式 AI 應用程式，以及如何使用提示設計和檢索增強生成 (RAG)，透過 LLM 建構強大的應用程式。我們也會介紹可用於正式環境的生成式 AI 應用程式架構。您將建構採用 LLM 和 RAG 的對話應用程式。
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Complete the Improve customer and agent satisfaction with Agent Assist skill badge to demonstrate your proficiency in configuring basic conversational agents that can escalate actions to human agents, and configuring Agent Assist to help human agents with customer queries. You prove your knowledge in configuring Generators for summarization, classification and recommendation of tickets as well leverage tools such as Generative Knowledge Assist, to provide further context to human agents.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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This course will focus on Agent Assist, an AI-powered tool designed to enhance customer service interactions. In this course, you will learn how Agent Assist can enhance the productivity of human agents while interacting with customers through the chat channel. You’ll learn how to take full advantage of Agent Assist from Gemini Enterprise for Customer Experience, and its range of Gen AI features and functionality.
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Connect conversational agents to external systems and APIs to expand what agents can do, designing an end-to-end system that is resilient, fault-tolerant and secure.
想瞭解 AI 背後的強大硬體嗎？本單元將深入解析針對效能最佳化的 AI 電腦，說明其重要性。我們將探討 CPU、GPU 和 TPU 如何大幅加速 AI 任務運算，分析各自的特點，以及 AI 軟體如何充分利用這些硬體效能。單元結束後，您將清楚掌握如何根據 AI 專案挑選合適的 GPU，並做出明智的 AI 工作負載決策。
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準備開始使用 AI Hypercomputer 了嗎？這門課程可讓您快速上手！我們將介紹這個架構的基本概念，以及此架構如何幫助 AI 處理 AI 工作負載。您將瞭解 Hypercomputer 內的不同元件，例如 GPU、TPU 和 CPU，以及如何視需求選擇合適的部署方法。
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This course equips learners with skills to govern data within their Google Workspace environment. Learners will explore data loss prevention rules in Gmail and Drive to prevent data leakage. They will then learn how to use Google Vault for data retention, preservation, and retrieval purposes. Next, they will learn how to configure data regions and export settings to align with regulations. Finally, learners will discover how to classify data using labels for enhanced organization and security.
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This course was designed to give learners a comprehensive understanding of Google Workspace core services. Learners will explore enabling, disabling, and configuring settings for these services, including Gmail, Calendar, Drive, Meet, Chat, and Docs. Next, they'll learn how to deploy and manage Gemini to empower their users. Finally, learners will examine use cases for AppSheet and Apps Script to automate tasks and extend the functionality of Google Workspace applications.
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This course empowers learners to secure their Google Workspace environment. Learners will implement strong password policies and two-step verification to govern user access. They will then utilize the security investigation tool to proactively identify and respond to security risks. Next, they will manage third-party app access and mobile devices to ensure security. Finally, learners will enforce email security and compliance measures to protect organizational data.
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This course was designed to provide an understanding of user and resource management in Google Workspace. Learners will explore the configuration of organizational units to align with their organization's needs. Additionally, learners will discover how to manage various types of Google Groups. They will also develop expertise in managing domain settings within Google Workspace. Finally, learners will master the optimization and structuring of resources within their Google Workspace environment.
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This course was designed to prepare Google Workspace Administrators to troubleshoot common Google Workspace issues. Learners will practice diagnosing and resolving problems in Gmail, Calendar, and Drive, and navigating the Admin console. They will also experience analyzing audit logs to troubleshoot security issues, and gathering information and using available resources to troubleshoot and report technical issues.
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery. Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners' understanding of the topics. "This learning path aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from Snowflake to BigQuery.
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Model tuning is an effective way to customize large models to your tasks. It's a key step to improve the model's quality and efficiency. Model tuning provides benefits such as higher quality results for your specific tasks and increased model robustness. You learn some of the tuning options available in Vertex AI and when to use them.
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This video covers how you can leverage Gemini's advanced AI capabilities within Google Sheets to effortlessly pull data and generate insights in minutes, all without the need for any technical or coding background.
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This video will cover how to leverage Gemini Gems to create authentic
social media posts in your leader's unique voice. Learn to overcome the
challenge of scaling executive social presence by training a Gem with
writing samples and clear instructions. Discover how to generate engaging
posts quickly, saving time while amplifying thought leadership and
ensuring authenticity.
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This video covers how you can create your own Brevity Gem to summarize and transform messy notes or long documents into clear, concise, executive-ready summaries.
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This video covers how you can leverage Notebook LM to "eat the frog" on your to-do list by automating complex tasks like summarizing legislation and mapping services, saving you hours of work.
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This video covers how to eliminate tedious manual data entry using Gemini. Learn how to take a picture or screenshot of data (from PDFs, paper, or images) and prompt Gemini to instantly convert it into a structured Google Sheet. Discover this simple hack to save countless hours transcribing data, turning Gemini into your personal data entry assistant. Just snap, prompt, and export!
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This video will cover how to use NotebookLM to gather and analyze
publicly available information, combine it with internal documents, and
extract key competitive insights.
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This video covers how you can use Gemini to summarize long documents in Google Workspace, so you can quickly get the information you need and save time. You'll learn how to use Gemini to summarize entire documents or just selected text, as well as how to use Gemini in Drive to summarize across multiple files.
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This video covers how NotebookLM can revolutionize customer insight gathering from call or chat transcripts. You'll learn to upload PDF transcripts of hundreds of conversations (even multilingual ones!) and quickly extract key themes, trending topics, and actionable insights without listening for hours. Discover how to save findings, share notebooks, and even generate interactive podcast summaries of your data.
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This video covers how to create your own Gemini Gems, advanced AI capabilities that can automate repetitive tasks and supercharge your productivity.
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助分析客戶資料及預測產品銷售情形。您也會學習如何在 BigQuery 中使用客戶資料識別、分類及開發新客戶。透過使用實作研究室，您可以體驗 Gemini 如何改良資料分析和機器學習工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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This course introduces AI Applications. You will learn about the types of apps that you can create using AI Applications, the high-level steps that its data stores automate for you, and what advanced features can be enabled for Search apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Learn about the fundamental features of Security Command Center on Google Cloud. Spend time in this course to understand assets, detection and compliance. Security Command Center is a key part of your Google Cloud security journey, complete these modules and quiz to earn a completion badge.
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This course covers the baseline skills needed for the Google Security Operations Platform. The modules will cover specific actions and features that security engineers should become familiar with to start using the toolset.
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Take the next steps in working with the Chronicle Security Operations Platform. Build on fundamental knowledge to go deeper on cusotmization and tuning.
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本課程會完整說明 Google Cloud Security Command Center (SCC) Enterprise，這是一項雲端原生應用程式保護平台 (CNAPP) 解決方案，可幫助組織在各項 Google Cloud 服務中預防、偵測及因應威脅。
您將瞭解 SCC Enterprise 的核心功能，包括增強型威脅偵測功能、深度安全漏洞管理，以及整合式案件管理機制。
本課程也會介紹威脅管理和安全漏洞評估的基礎概念，並實際示範如何運用 SCC Enterprise 辨別、調查及修正多雲端環境中的安全風險。
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This training aims to up-skill Google Cloud partners to deliver customer engagements through Delivery Navigator for available technical practice offerings. Learners will be able to navigate around the Delivery Navigator platform, select the desired method(s), and export the project WBS to a desired work management tool and Shared Google Drive. Sample artefacts are available through the Delivery Navigator methods and will be provided for reference.
Contents of this course will be updated as new features are released for the Delivery Navigator platform.
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Google Threat Intelligence provides unmatched visibility into threats by delivering detailed and timely threat intelligence to security teams around the world. This course covers the various capabilities of Google Threat Intelligence and common ways that organizations use this product to proactively mitigate threats.
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This course gives you a deep dive into the workflows of Tier 3 analysts.
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This course explores the quality assurance best practices and the tools available in Conversational Agents to ensure production grade quality during Conversational Agent development, as well as the key tenets for the creation of a robust end to end deployment lifecycle. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.
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In this course, you'll learn to develop AI agents that answer questions using websites, documents, or structured data. You will explore AI Applications and understand the advantages of data store agents, including their scalability and security.
You'll learn about different data store types and also discover how to connect data stores to agents and add personalization for enhanced responses.
Finally, you'll gain insights into common search configurations and troubleshooting techniques.
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This course gives you a deep dive into the workflows of Tier 1 and Tier 2 security analysts.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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This course will provide you with an overview of SIEM technology to set the stage for the differentiation and expansion of capabilities that Chronicle SIEM provides.
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Learn which Mandiant products directly enhance or augment capabilities provided by Chronicle SIEM and SOAR and how those products integrate into our workflow.
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「生成式 AI：掌握幕後技術與環境」是 Generative AI Leader 學習路徑的第三門課程。生成式 AI 正在改變我們的工作方式,以及我們如何與周遭的世界互動。身為領導者,您要如何駕馭 AI 強大的功能,創造實際業務成果?在本課程中,您將認識建構生成式 AI 解決方案時的各個層面、Google Cloud 產品,以及選擇解決方案時應考量的因素。
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「生成式 AI 代理：實現組織轉型」是 Gen AI Leader 學習路徑的第五堂也是最後一堂課程。本課程將探討組織如何運用自訂生成式 AI 代理，解決特定的業務難題。您將動手練習建構基本的生成式 AI 代理，同時探索這類代理的各種元件，例如模型、推論迴圈和工具。
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「生成式 AI 應用程式：徹底改變工作方式」是 Generative AI Leader 學習路徑的第四門課程。本課程將介紹 Google 的生成式 AI 應用程式,例如 Gemini for Workspace 和NotebookLM,也會引導您瞭解各種概念,像是建立基準、檢索增強生成、建構有效的提示詞,以及打造自動化工作流程等。
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「生成式 AI: 瞭解基礎概念」是 Generative AI Leader 學習路徑的第二門課程。在本課程中,您將瞭解 AI、機器學習和生成式 AI 的差異,以及各種資料類型如何協助生成式 AI 解決業務難題,進而掌握生成式 AI 的基礎概念。您還能深入瞭解 Google Cloud 應對基礎
模型限制的策略,以及開發、部署安全且負責任的 AI 技術時面臨的主要挑戰。
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「生成式 AI：不只是聊天機器人」是 Generative AI Leader 學習路徑的第一門課程,沒有任何修課條件。本課程將帶您超越基本知識,進一步瞭解聊天機器人,探索如何在組織中充分發揮生成式 AI 的潛力。您將瞭解基礎模型和提示工程等概念,掌握善用生成式AI 的關鍵。本課程也會帶您瞭解擬定生成式 AI 策略時的多種重要考量,協助您為組織擬定出成功的策略。
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Welcome to the fourth course of the "Networking in Google Cloud" series: Network Security!
In this course, you'll dive into the services for safeguarding your Google Cloud network infrastructure.
The first module, Distributed Denial of Service (DDoS) Protection, covers how to fortify your network against Distributed Denial of Service (DDoS) attacks, ensuring uninterrupted availability of your services.
In the second module, Controlling Access to VPC Networks, you'll learn the network access control, enabling you to define permissions for who can access your resources and how.
Finally, in the third module, Advanced Security Monitoring and Analysis, we'll explore how to proactively detect and respond to potential threats, keeping your Google Cloud environment secure and resilient.
By the end of this course, you'll have a comprehensive understanding of Google Cloud network security.
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本課程說明如何使用 Google Agent Development Kit 建構複雜的多代理系統。您將建構配備工具的虛擬服務專員，並透過從屬關係和流程定義互動方式。您將在本機執行代理，並部署至 Vertex AI Agent Engine，透過代管代理流程執行；Agent Engine 則處理基礎架構決策和資源調度作業。
請注意，這些實驗室是根據這項產品的預先發布版製成。我們會進行維護更新，因此這些研究室將可能出現延遲。
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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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Discover how to use Colab Enterprise, a managed notebook environment that provides secure and compliant storage for your notebooks, that comes with two code-generation features: code complete and code gen. Create and use runtime templates in Vertex AI Workbench to give users access to more powerful compute resources while still maintaining control over the types of resources that are spun up. Share notebooks with other users and use versioning to keep track of changes to your notebooks. Learn how Colab Enterprise integrates BigQuery and Vertex AI. You will see how to pull data from BigQuery, use BQML to train a model, and have it all integrated with Vertex Model Registry. Explore how to fine-tune a Foundation model or generative AI model using the Vertex AI SDK. And, learn how to evaluate a tuned model and compare the results of multiple runs.
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Embark on a journey into the captivating world of embeddings! This course equips you with the theoretical and practical knowledge to harness their power in both product search and generative AI.
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這堂課程會說明 BigQuery 中的檢索增強生成 (RAG) 解決方案，協助您減少 AI 幻覺。當中介紹的 RAG 工作流程包含建立嵌入項目、搜尋向量空間，以及生成更符合需求的答案。另外，這堂課程會解釋這些步驟背後的概念與原因，以及實際運用 BigQuery 實作的方法。完成課程之後，學員將學會使用 BigQuery，以及 Gemini 和嵌入模型等生成式 AI 模型，建立 RAG pipeline 來處理自己的 AI 幻覺應用實例。
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with building a Custom Doc Extractor using the Google Cloud AI solution. The following will be addressed:
Service: Document AI
Task: Extract fields
Processors: Custom Document Extractor and Document Splitter
Prediction: Using Endpoint to programmatically extract fields
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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.
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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.
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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.
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This course explores the Geographic Information Systems (GIS), GIS Visualization, and machine learning enhancements to BigQuery.
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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.
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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.
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Identify critical assets and their compliance requirements.
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This training course introduces Cloud NGFW. Topics include how Cloud NGFW provides centralized firewall management, centralized firewall visibility, advanced threat protection, and firewall insights.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases.
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This course is for Google Cloud’s top partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases. Those who complete the training and assessment will receive the Google Cloud Generative AI Trailblazer badge through Skills Boost.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.
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本課程旨在提供必要的知識和工具，協助您探索機器學習運作團隊在部署及管理生成式 AI 模型時面臨的獨特挑戰，並瞭解 Vertex AI 如何幫 AI 團隊簡化機器學習運作程序，打造成效非凡的生成式 AI 專案。
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This on-demand course provides partners the skills required to design, deploy, and monitor Vertail AI Search for Commerce solutions including retail search and recommendation AI for enterprise customers.
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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。
您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。
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In this skill badge, you will demonstrate your ability to deploy Google Agentspace and set up data stores and actions. To learn these skills, we encourage you to take the course Accelerate Knowledge Exchange with Agentspace.
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Gemini Enterprise 結合 Google 的搜尋和 AI 輔助功能，企業員工只要在單一搜尋列輸入關鍵字，就能查找文件儲存空間、電子郵件、對話、支援單處理系統和其他資料來源中的特定資訊。Gemini Enterprise 助理還能協助人員腦力激盪、研究資訊、列出文件大綱及執行其他動作，例如邀請同事加入日曆活動，加快完成知識型工作及各種協作作業。(請注意，Gemini Enterprise 先前稱為 Google Agentspace，本課程可能會提及產品舊稱。)
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Do you want to keep your users engaged by suggesting content they'll love?
This course equips you with the skills to build a cutting-edge recommendations app using your own data with no prior machine learning knowledge. You learn to leverage AI Applications to build recommendation applications so that audiences can discover more personalized content, like what to watch or read next, with Google-quality results customized using optimization objectives.
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This course explores the different products and capabilities of Gemini Enterprise for Customer Experience and Conversational Agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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Explore Playbooks and their implementation of the ReAct pattern for building conversational agents. You will learn how to construct a Playbook, set up goals and instructions to build a chatbot in natural language, and learn to test and deploy your solution.
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Imagen provides a suite of generative AI tools to help you accelerate your creative workflows. This course provides you with demonstrations of all the key features currently found in Imagen.
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In this course you will learn the key architectural considerations that need to be taken into account when designing for the implementation of Conversational AI solutions.
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Learn about building conversational AI voice and chat integrations, including how telephony systems can connect with Google to enable phone-based interactions within the Conversational AI ecosystem. Explore key topics such as the differences between chat and voice conversations, the writing process for creating conversation scripts, and the beginning of the interrogative series and closing sequence.
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This course will equip you with the tools to develop complex conversational experiences in Conversational Agents capable of identifying the user intent and routing it to the right self service flow. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.
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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
Welcome to the third course of the "Networking in Google Cloud" series: Network Architecture!
In this course, you will explore the fundamentals of designing efficient and scalable network architectures within Google Cloud.
In the first module, Introduction to Network Architecture, we'll start by introducing you to the core components and concepts of network architecture, including subnets, routes, firewalls, and load balancing.
Then in the second module, network topologies, we'll dive into various network topologies commonly used in Google Cloud, discussing their strengths, and weaknesses.
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Welcome to the second course in the networking and Google Cloud series routing and addressing.
In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities.
Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses.
By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能，著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具，並透過互動式練習，協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series.
In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area.
You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training.
This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.
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Gemini 版 Google Workspace 是一項外掛程式，可讓使用者存取生成式 AI 功能。本課程會使用影片、實作活動和練習範例，深入介紹 Gemini 版 Google Meet 的功能。您會學到如何透過 Gemini 生成背景圖片、提升視訊品質及翻譯字幕。本課程結束後，您將具備 Gemini 版 Google Meet 的知識及技能，安心運用這項工具提高視訊會議的效率。
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This course helps you structure your preparation for the Professional Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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This course educates partners on key concepts around deploying Google Cloud VMware Engine (GCVE) and leveraging HCX to migrate VMs from on-premises VMware to GCVE.
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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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In this course, we’ll show you how organizations are aligning their BI strategy to most effectively achieve business outcomes with Looker. We'll follow four iterative steps: Plan, Build, Launch, Grow, and provide resources to take into your own services delivery to build Looker with the goal of achieving business outcomes.
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There's much excitement about cloud technology and digital transformation, but often many unanswered questions.
For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin?
If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.
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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task.
Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences.
“Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.
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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.
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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls.
This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.
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安裝 Gemini 版 Google Workspace 外掛程式後，客戶就能在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能，並說明如何在 Google 試算表善用這些功能，提高生產力和效率。
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使用者將能透過 Gemini 版 Google Workspace 外掛程式運用生成式 AI 功能。本課程會使用影片、實作活動和練習範例，深入介紹 Gemini 版 Google 文件的功能。您將學到如何透過 Gemini 使用提示生成撰寫內容、編輯寫好的文字，以提升整體工作效率。本課程結束後，您將具備 Gemini 版 Google 文件的知識及技能，可自信地運用這項工具提升寫作品質。
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Gemini 版 Google Workspace 是一項外掛程式，可讓客戶在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能，並說明如何在 Gmail 善用這些功能，提高生產力和效率。
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客戶能透過 Gemini 版 Google Workspace 外掛程式在 Google Workspace 使用生成式 AI 功能。本學習路徑會介紹 Gemini 的主要功能，並說明如何在 Google Workspace 善用這些功能，提高生產力和效率。
本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud Security Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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Learn the technical aspects you need to know about Chronicle and how it can help you detect and action threats.
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This course is the third part of the SAP on Google Cloud Platform learning path. Following the SAP on Google Cloud Foundations eLearning and the SAP on Google Cloud Self-paced labs. Participants should have completed these two components before. This course consists of hands-on labs that provide a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud for their customers.
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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
[DEPRECATED] SOAR Fundamentals
Earned Dec 9, 2025 EST
Model Armor：保護您部署的 AI 應用程式
Earned Dec 5, 2025 EST
Google Threat Intelligence
Earned Dec 5, 2025 EST
Security Command Center: Identify and Prioritize Risks
Earned Dec 5, 2025 EST
Build a Certification Study Guide: PSOE Exam Prep
Earned Dec 5, 2025 EST
Find, Explore and Deploy Model Garden Models
Earned Nov 7, 2025 EST
Engineer Effective Prompts for Generative Models
Earned Nov 7, 2025 EST
Explore Google's Gen AI Models
Earned Nov 7, 2025 EST
Model evaluation on Vertex AI
Earned Nov 7, 2025 EST
Create Data Stores for Gen AI Applications
Earned Nov 7, 2025 EST
Implement Hybrid Search
Earned Nov 5, 2025 EST
Enterprise Readiness in Generative AI
Earned Nov 4, 2025 EST
Evaluate ADK Agents with Vertex AI Gen AI Evaluation Service
Earned Nov 4, 2025 EST
Scale AI with Ray on Vertex AI
Earned Nov 4, 2025 EST
Implement RAG with Vertex AI
Earned Nov 4, 2025 EST
Vertex AI Search and Gemini Enterprise UI Configurations
Earned Nov 3, 2025 EST
How Google Does Machine Learning
Earned Nov 3, 2025 EST
Google DeepMind: 04 Discover The Transformer Architecture
Earned Nov 2, 2025 EST
Vertex AI Search and Gemini Enterprise Analytics
Earned Oct 20, 2025 EDT
Generate and Edit Media in Vertex AI
Earned Sep 17, 2025 EDT
透過 Gemini in BigQuery 提升工作效率
Earned Sep 16, 2025 EDT
透過 BigQuery 機器學習執行推論作業
Earned Sep 16, 2025 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub
Earned Sep 16, 2025 EDT
Introduction to NotebookLM
Earned Sep 16, 2025 EDT
Azure Virtual Machines to Compute Engine
Earned Sep 15, 2025 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Sep 14, 2025 EDT
運用 BigQuery ML 建立機器學習模型
Earned Sep 14, 2025 EDT
Custom Search with Embeddings in Vertex AI
Earned Sep 14, 2025 EDT
Vector Search 和嵌入
Earned Sep 14, 2025 EDT
Vertex AI Studio 簡介
Earned Sep 14, 2025 EDT
Transformer 和 BERT 模型
Earned Sep 14, 2025 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Sep 13, 2025 EDT
Build a Certification Study Guide: PMLE
Earned Sep 13, 2025 EDT
Working with Notebooks in Vertex AI
Earned Sep 11, 2025 EDT
生成式 AI 簡介
Earned Sep 10, 2025 EDT
大型語言模型簡介
Earned Sep 10, 2025 EDT
機器學習運作 (MLOps) 與 Vertex AI：模型評估
Earned Sep 10, 2025 EDT
開發人員的負責任 AI 技術：可解釋性與透明度
Earned Sep 10, 2025 EDT
開發人員的負責任 AI 技術：隱私權與安全性
Earned Sep 10, 2025 EDT
開發人員的負責任 AI 技術：公平性與偏誤
Earned Sep 9, 2025 EDT
在 Google Cloud 打造生成式 AI 應用程式
Earned Sep 9, 2025 EDT
Improve customer and agent satisfaction with Agent Assist
Earned Sep 4, 2025 EDT
Introduction to Agent Assist and its GenAI Capabilities
Earned Sep 4, 2025 EDT
Extend Conversational Agents Functionality with Webhooks and Tools
Earned Aug 15, 2025 EDT
AI 基礎架構：Cloud TPU
Earned Aug 7, 2025 EDT
AI 基礎架構：Cloud GPU
Earned Aug 7, 2025 EDT
AI 基礎架構：AI Hypercomputer 簡介
Earned Aug 7, 2025 EDT
Google Workspace Data Governance
Earned Aug 7, 2025 EDT
Google Workspace Core Services
Earned Aug 7, 2025 EDT
Google Workspace Security
Earned Aug 7, 2025 EDT
Google Workspace User and Resource Management
Earned Aug 7, 2025 EDT
Google Workspace Troubleshooting
Earned Aug 5, 2025 EDT
Snowflake to BigQuery Migration
Earned Jun 16, 2025 EDT
Improve Performance by Fine-Tuning Foundation Models
Earned Jun 15, 2025 EDT
AI Boost Bites: No-Code Sheets & Scripts
Earned Jun 13, 2025 EDT
AI Boost Bites: Amplify Exec Voices with AI
Earned Jun 13, 2025 EDT
AI Boost Bites: Exec Summaries with Gemini Gems
Earned Jun 13, 2025 EDT
AI Boost Bites: "Eat the Frog" with NotebookLM
Earned Jun 13, 2025 EDT
AI Boost Bites: Gemini Image-to-Sheets Hack
Earned Jun 13, 2025 EDT
AI Boost Bites: NotebookLM for Competitive Edge
Earned Jun 13, 2025 EDT
AI Boost Bites: TL;DR with Gemini in Docs & Drive
Earned Jun 13, 2025 EDT
AI Boost Bites: Customer insights with NotebookLM
Earned Jun 13, 2025 EDT
AI Boost Bites: Gemini Gems – Your ultimate marketing sidekick
Earned Jun 13, 2025 EDT
使用 Gemini：數據資料學家和分析師
Earned Jun 13, 2025 EDT
Introduction to AI Applications
Earned May 22, 2025 EDT
Security Command Center Fundamentals
Earned May 15, 2025 EDT
Google Security Operations - Fundamentals
Earned May 15, 2025 EDT
Google Security Operations - Deep Dive
Earned May 14, 2025 EDT
開始使用 Security Command Center Enterprise
Earned May 14, 2025 EDT
Delivery Navigator for Partners
Earned May 14, 2025 EDT
DEPRECATED Google Threat Intelligence
Earned May 13, 2025 EDT
SecOps on GDC for Tier 3 Analysts
Earned May 10, 2025 EDT
Conversational Agents Quality Assurance and Deployment Lifecycle
Earned May 7, 2025 EDT
Extend CX Agents with Vertex AI Search data stores
Earned May 7, 2025 EDT
SecOps on GDC for Tier 1 and Tier 2 Analysts
Earned Apr 26, 2025 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Apr 23, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned Apr 21, 2025 EDT
Chronicle SIEM Fundamentals
Earned Apr 17, 2025 EDT
Mandiant Fundamentals
Earned Apr 17, 2025 EDT
生成式 AI：掌握幕後技術與環境
Earned Apr 17, 2025 EDT
生成式 AI 代理：實現組織轉型
Earned Apr 17, 2025 EDT
生成式 AI 應用程式：徹底改變工作方式
Earned Apr 17, 2025 EDT
生成式 AI：瞭解基礎概念
Earned Apr 17, 2025 EDT
生成式 AI：不只是聊天機器人
Earned Apr 17, 2025 EDT
Networking in Google Cloud: Network Security
Earned Apr 13, 2025 EDT
運用 Agent Development Kit (ADK) 與 Agent Engine 部署多代理系統
Earned Apr 10, 2025 EDT
The Modern Data Platform and LookML
Earned Mar 17, 2025 EDT
Natural Language Processing on Google Cloud
Earned Mar 10, 2025 EDT
Feature Engineering
Earned Mar 8, 2025 EST
建立圖像說明生成模型
Earned Mar 8, 2025 EST
Production Machine Learning Systems
Earned Mar 8, 2025 EST
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Mar 7, 2025 EST
Launching into Machine Learning
Earned Mar 7, 2025 EST
Building AI with Colab Enterprise
Earned Mar 7, 2025 EST
Vector Search and Embeddings in Product Search
Earned Mar 2, 2025 EST
運用 BigQuery 建立嵌入項目、向量搜尋和 RAG
Earned Mar 2, 2025 EST
Document AI: Building a Custom Document Extractor
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Process Data with Dataproc
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Optimize in BigQuery
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Design in BigQuery
Earned Feb 27, 2025 EST
Data Warehousing for Partners: BigQuery Extended Capabilities
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Enable Google Cloud Customers
Earned Feb 27, 2025 EST
Data Warehousing for Partners: Streaming Analytics
Earned Feb 27, 2025 EST
Evaluate Your Cloud Next Generation Firewall Needs
Earned Feb 23, 2025 EST
Introduction to Cloud Next Generation Firewall
Earned Feb 23, 2025 EST
Unlocking the Power of Google Cloud Generative AI for Partners
Earned Feb 22, 2025 EST
Google Cloud Generative AI Trailblazer
Earned Feb 22, 2025 EST
Security Best Practices in Google Cloud
Earned Feb 22, 2025 EST
生成式 AI 適用的機器學習運作 (MLOps)
Earned Feb 20, 2025 EST
Vertex AI Search for Commerce
Earned Feb 19, 2025 EST
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Feb 19, 2025 EST
Deploy Google Agentspace
Earned Feb 18, 2025 EST
運用 Gemini Enterprise 加速知識交流
Earned Feb 18, 2025 EST
Recommendations with AI Applications
Earned Feb 18, 2025 EST
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned Feb 18, 2025 EST
Generative Playbooks
Earned Feb 16, 2025 EST
Accelerate Your Creativity with Text-to-Image using Imagen
Earned Feb 16, 2025 EST
Customer Experience with Google AI Architecture
Earned Feb 13, 2025 EST
Conversational AI Voice and Chat Integrations
Earned Feb 12, 2025 EST
Building Complex Self-Service Experiences in Conversational Agents
Earned Feb 11, 2025 EST
Search with AI Applications
Earned Feb 1, 2025 EST
在 BigQuery 使用 Gemini 模型
Earned Jan 23, 2025 EST
Networking in Google Cloud: Network Architecture
Earned Oct 4, 2024 EDT
Networking in Google Cloud: Routing and Addressing
Earned Oct 4, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Sep 27, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介
Earned Jun 16, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Jun 15, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 15, 2024 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jun 1, 2024 EDT
Gemini in Google Meet
Earned May 30, 2024 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned May 16, 2024 EDT
Migrating VMware to Google Cloud VMware Engine
Earned May 16, 2024 EDT
Modernizing Mainframe Applications with Google Cloud
Earned May 16, 2024 EDT
Developing a Google SRE Culture
Earned Apr 12, 2024 EDT
Achieving Business Outcomes with Looker
Earned Apr 3, 2024 EDT
Digital Transformation with Google Cloud
Earned Mar 18, 2024 EDT
Trust and Security with Google Cloud
Earned Mar 18, 2024 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Mar 18, 2024 EDT
Innovating with Google Cloud Artificial Intelligence
Earned Mar 18, 2024 EDT
Scaling with Google Cloud Operations
Earned Mar 17, 2024 EDT
Exploring Data Transformation with Google Cloud
Earned Mar 17, 2024 EDT
Analyzing and Visualizing Data in Looker
Earned Mar 17, 2024 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Mar 16, 2024 EDT
BI Reporting: Looker Visualization on BigQuery
Earned Mar 16, 2024 EDT
Certification Learning Path: Professional Cloud DevOps Engineer
Earned Mar 13, 2024 EDT
Managing Security in Google Cloud
Earned Mar 1, 2024 EST
Gemini in Google Sheets
Earned Jan 12, 2024 EST
Gemini in Google Docs
Earned Jan 12, 2024 EST
Gemini in Gmail
Earned Jan 12, 2024 EST
Gemini for Google Workspace 簡介
Earned Jan 12, 2024 EST
編碼器-解碼器架構
Earned Dec 28, 2023 EST
注意力機制
Earned Dec 28, 2023 EST
圖像生成簡介
Earned Dec 28, 2023 EST
負責任的 AI 技術簡介
Earned Dec 22, 2023 EST
Certification Learning Path: Professional Cloud Security Engineer
Earned Oct 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 29, 2023 EDT
Security Practices with Google Security Operations - SIEM
Earned Sep 25, 2023 EDT
SAP on Google Cloud
Earned Sep 21, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Sep 6, 2023 EDT